Leave No Trace
Many hikers, campers, and other outdoor enthusiasts know the saying, “Leave No Trace”. The National Park Service publishes websites and physical cards explaining the principles of Leave No Trace (2022). The seven principles are:
- Plan Ahead and Prepare
- Travel and Camp on Durable Surfaces
- Dispose of Waste Properly
- Leave What You Find
- Minimize Campfire Impacts
- Respect Wildlife
- Be Considerate of Other Visitors
(2022)
You can find more information about the principles here. While everyone exploring national parks and nature should know them all, the idea is to leave things the way you found them and ensure others can enjoy natural spaces as you did after you left.
Unfortunately, we live in a world where people affect change in areas beyond where they live and visit. Industry changes have impacted the climate and conditions on the other side of the world. We can no longer ensure that future generations will be able to enjoy the outdoors like we have without making some changes.
Predicted Data
This page uses data from predicted data. The projected data is split into two terms: near-term from 2021 to 2059 and long-term from 2060 to 2099. The near-term and long-term data were predicted using two Representative Concentration Pathways (RCPs) and twenty General Circulation Model (GCM) scenarios (2022). The two RCPs are based on a medium mitigation and stabilization scenario, RCP4.5, and a high baseline, no-policy scenario, RCP8.5 (2022). The twenty GCM scenarios are built based on models from SOILWAT2. You can find more information on the models here. (2023). The predicted data shown in the plots below are from the twenty GCMs are averaged together to give a single value for each observation for the RCP4.5 and RCP8.5 predictions.
Water Availability
All life on Earth needs water to survive. In this section, we will compare annual precipitation and the amount of water available in the top 50 cm of soil. These two features should provide valuable insights into whether water is available to animals and plants in the area.
Precipitation
The three plots below show the average annual precipitation among all observation sites for the historical data, predicted near-term data and predicted long-term data. Averaging the twenty models together reduces the variance in the predicted data, leading to a higher mean annual precipitation and lower variance than the historical data. Based on these charts and a look at the basic statistics during EDA. The future will have little impact on the amount of precipitation.
Soil Water Availability
Once again, the data we saw in EDA and these plots suggest that there will be plenty of water available for plants and animals in the future. To be sure, we will use Welch’s T-Test to compare the three different data sets.
T-Test
Here, we use Welch’s T-Test, to compare historical data to near term and long term data. Below are our null and alternative hypotheses.
\(H_0:\) There is no difference in soil water availability between historical and near/long term data.
\(H_A\):$ There is a difference in soil water availability between historical and near/long term data.
The t-statistics for both tests shown below have very small p-values, indicating strong evidence to reject the null hypotheses. These t-test suggest that there is a significant difference in soil water availability. The 95 percent confidence for the true difference in mean reveals that the predicted near and long term data has a higher soil water availability.
Code
%%R
#import libray
library(tidyverse)
#import data
df <- read.csv('../data/data.csv')
#filter data
hist <- df[df$TimePeriod == "Hist", ]
nt <- df[df$TimePeriod == "NT", ]
lt <- df[df$TimePeriod == "LT", ]
#test hist vs near term
print("Welch's T-Test: Historical vs Near Term Data")
print(t.test(hist$SWA_Fall_top50, nt$SWA_Fall_top50, alternative = "two.sided"))
#test hist vs lt
print("Welch's T-Test: Historical vs Long Term Data")
print(t.test(hist$SWA_Fall_top50, lt$SWA_Fall_top50, alternative = "two.sided"))[1] "Welch's T-Test: Historical vs Near Term Data"
Welch Two Sample t-test
data: hist$SWA_Fall_top50 and nt$SWA_Fall_top50
t = -11.034, df = 4723.2, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.2174275 -0.1518233
sample estimates:
mean of x mean of y
1.348573 1.533198
[1] "Welch's T-Test: Historical vs Long Term Data"
Welch Two Sample t-test
data: hist$SWA_Fall_top50 and lt$SWA_Fall_top50
t = -13.273, df = 4719.9, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.2548388 -0.1892461
sample estimates:
mean of x mean of y
1.348573 1.570615
Temperature
Temperature is a great indicator of how life in Natural Bridges National Monument will perform over the near and long term. A higher temperature will lead to increased water consumption and reduced activity. As temperatures rise, plants and animals must increase their water intake to face life-threatening consequences. Animals may also have to limit their activity, which could lead to decreased food consumption and cause them to pass.
Annual Mean Temperature
The following plots and t-test examine the mean annual temperature. The mean is calculated by finding the mean of every observed temperature from each collection site per season and using the seasonal means to calculate an annual mean. The plots and t-test show a frightening trend for the future.
T-Test
Once again we use Welch’s T-Test, this time to compare historical mean annual temperature to near term and long term mean annual temperature. Below are our null and alternative hypotheses.
\(H_0:\) There is no difference in maximum summer temperature between historical and near/long term data.
\(H_A\):$ There is a difference in maximum summer temperature between historical and near/long term data.
Code
%%R
#test hist vs near term
print("Welch's T-Test: Historical vs Near Term Data")
print(t.test(hist$T_Annual, nt$T_Annual, alternative = "two.sided"))
#test hist vs lt
print("Welch's T-Test: Historical vs Long Term Data")
print(t.test(hist$T_Annual, lt$T_Annual, alternative = "two.sided"))[1] "Welch's T-Test: Historical vs Near Term Data"
Welch Two Sample t-test
data: hist$T_Annual and nt$T_Annual
t = -178.1, df = 4928.7, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-2.151217 -2.104373
sample estimates:
mean of x mean of y
10.83586 12.96366
[1] "Welch's T-Test: Historical vs Long Term Data"
Welch Two Sample t-test
data: hist$T_Annual and lt$T_Annual
t = -317.17, df = 5583.6, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-3.933786 -3.885456
sample estimates:
mean of x mean of y
10.83586 14.74548
Summer Maximum Temperature
These plots and t-tests use the highest temperature observations from all collection sites over the summer to find the maximum summer temperature. If things looked bad while viewing the mean annual temperature, this is worse. With long term max temperatures exceeding 120 degrees Fahrenheit almost every year, many healthy people cannot function normally at these temperatures, and those not in their prime face disastrous consequences when temperatures reach this high.
T-Test
Here, we use Welch’s T-Test, to compare historical maximum summer temperature to near term and long term maximum summer temperature. Below are our null and alternative hypotheses.
\(H_0:\) There is no difference in maximum summer temperature between historical and near/long term data.
\(H_A\):$ There is a difference in maximum summer temperature between historical and near/long term data.
The test shows a tiny p-value, indicating strong evidence to reject the null hypotheses. The two t-test show with a 95 percent confidence interval that the near and long term maximum summer temperature is higher than the historical maximum summer temperature. The Long term mean maximum summer temperature is almost 5 degrees Celsius warmer than the historical mean.
Code
%%R
#test hist vs near term
print("Welch's T-Test: Historical vs Near Term Data")
print(t.test(hist$Tmax_Summer, nt$Tmax_Summer, alternative = "two.sided"))
#test hist vs lt
print("Welch's T-Test: Historical vs Long Term Data")
print(t.test(hist$Tmax_Summer, lt$Tmax_Summer, alternative = "two.sided"))[1] "Welch's T-Test: Historical vs Near Term Data"
Welch Two Sample t-test
data: hist$Tmax_Summer and nt$Tmax_Summer
t = -116.47, df = 4992, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-2.457613 -2.376249
sample estimates:
mean of x mean of y
35.90902 38.32595
[1] "Welch's T-Test: Historical vs Long Term Data"
Welch Two Sample t-test
data: hist$Tmax_Summer and lt$Tmax_Summer
t = -207.78, df = 5608.9, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-4.481333 -4.397560
sample estimates:
mean of x mean of y
35.90902 40.34847
Conclusion
No longer can we only worry about our direct impact on nature. “Leave No Trace” is no longer sufficient to ensure future generations can view the natural beauty or access the ancient sites of a lost people. We need to do more to influence policy to ensure our children and theirs will be able to have the same experiences and joys we did at parks like the Natural Bridges National Monument.
References
Ecology, Dryland. 2023. “SOILWAT2.” October 24, 2023. https://github.com/DrylandEcology/SOILWAT2.
Service, National Park. 2022. “Leave No Trace Seven Principles.” May 16, 2022. https://www.nps.gov/articles/leave-no-trace-seven-principles.htm.
———. 2024. “Natural Bridges.” April 25, 2024. https://www.nps.gov/nabr/index.htm.
Survey, U. S. Geological. 2022. “Climate and Drought Adaptation: Historical and Projected Future Exposure Metrics for Southeastern Utah Group National Parks.” March 2, 2022. https://www.sciencebase.gov/catalog/file/get/61a6952fd34eb622f6978d9f?f=__disk__78%2F99%2F9b%2F78999b749568b2fbba86ce5dc9fc89aebe469388&transform=1&allowOpen=true.